Title

from fastai.vision.all import *

path=untar_data(URLs.PETS)/'images'

files=get_image_files(path) # 이미지파일들의 이름을 모두 복붙하여 리스트를 만든뒤에 files.txt로 저장하는 과정으로 비유할 수 있음

files[2] # txt 파일의 3번째 목록

Path('/home/cgb4/.fastai/data/oxford-iiit-pet/images/leonberger_5.jpg')

def label_func(f):
    if f[0].isupper():
        return 'cat' 
    else: 
        return 'dog' 

label_func('asdf')

'dog'

dls=ImageDataLoaders.from_name_func(path,files,label_func,item_tfms=Resize(224)) 

dls.show_batch(max_n=16)

learn=cnn_learner(dls,resnet34,metrics=error_rate)

Downloading: "https://download.pytorch.org/models/resnet34-b627a593.pth" to C:\Users\cgb/.cache\torch\hub\checkpoints\resnet34-b627a593.pth

---------------------------------------------------------------------------
ImportError                               Traceback (most recent call last)
~\AppData\Local\Temp/ipykernel_29964/2234347239.py in <module>
----> 1 learn=cnn_learner(dls,resnet34,metrics=error_rate)

~\anaconda3\envs\bda2021\lib\site-packages\fastai\vision\learner.py in cnn_learner(dls, arch, normalize, n_out, pretrained, config, loss_func, opt_func, lr, splitter, cbs, metrics, path, model_dir, wd, wd_bn_bias, train_bn, moms, **kwargs)
    177     if n_out is None: n_out = get_c(dls)
    178     assert n_out, "`n_out` is not defined, and could not be inferred from data, set `dls.c` or pass `n_out`"
--> 179     model = create_cnn_model(arch, n_out, pretrained=pretrained, **kwargs)
    180 
    181     splitter=ifnone(splitter, meta['split'])

~\anaconda3\envs\bda2021\lib\site-packages\fastai\vision\learner.py in create_cnn_model(arch, n_out, pretrained, cut, n_in, init, custom_head, concat_pool, **kwargs)
    141     "Create custom convnet architecture"
    142     meta = model_meta.get(arch, _default_meta)
--> 143     body = create_body(arch, n_in, pretrained, ifnone(cut, meta['cut']))
    144     if custom_head is None:
    145         nf = num_features_model(nn.Sequential(*body.children()))

~\anaconda3\envs\bda2021\lib\site-packages\fastai\vision\learner.py in create_body(arch, n_in, pretrained, cut)
     63 def create_body(arch, n_in=3, pretrained=True, cut=None):
     64     "Cut off the body of a typically pretrained `arch` as determined by `cut`"
---> 65     model = arch(pretrained=pretrained)
     66     _update_first_layer(model, n_in, pretrained)
     67     #cut = ifnone(cut, cnn_config(arch)['cut'])

~\anaconda3\envs\bda2021\lib\site-packages\torchvision\models\resnet.py in resnet34(pretrained, progress, **kwargs)
    286         progress (bool): If True, displays a progress bar of the download to stderr
    287     """
--> 288     return _resnet('resnet34', BasicBlock, [3, 4, 6, 3], pretrained, progress,
    289                    **kwargs)
    290 

~\anaconda3\envs\bda2021\lib\site-packages\torchvision\models\resnet.py in _resnet(arch, block, layers, pretrained, progress, **kwargs)
    260     model = ResNet(block, layers, **kwargs)
    261     if pretrained:
--> 262         state_dict = load_state_dict_from_url(model_urls[arch],
    263                                               progress=progress)
    264         model.load_state_dict(state_dict)

~\anaconda3\envs\bda2021\lib\site-packages\torch\hub.py in load_state_dict_from_url(url, model_dir, map_location, progress, check_hash, file_name)
    551             r = HASH_REGEX.search(filename)  # r is Optional[Match[str]]
    552             hash_prefix = r.group(1) if r else None
--> 553         download_url_to_file(url, cached_file, hash_prefix, progress=progress)
    554 
    555     if _is_legacy_zip_format(cached_file):

~\anaconda3\envs\bda2021\lib\site-packages\torch\hub.py in download_url_to_file(url, dst, hash_prefix, progress)
    436         if hash_prefix is not None:
    437             sha256 = hashlib.sha256()
--> 438         with tqdm(total=file_size, disable=not progress,
    439                   unit='B', unit_scale=True, unit_divisor=1024) as pbar:
    440             while True:

~\anaconda3\envs\bda2021\lib\site-packages\tqdm\notebook.py in __init__(self, *args, **kwargs)
    240         unit_scale = 1 if self.unit_scale is True else self.unit_scale or 1
    241         total = self.total * unit_scale if self.total else self.total
--> 242         self.container = self.status_printer(self.fp, total, self.desc, self.ncols)
    243         self.container.pbar = proxy(self)
    244         self.displayed = False

~\anaconda3\envs\bda2021\lib\site-packages\tqdm\notebook.py in status_printer(_, total, desc, ncols)
    113         # Prepare IPython progress bar
    114         if IProgress is None:  # #187 #451 #558 #872
--> 115             raise ImportError(
    116                 "IProgress not found. Please update jupyter and ipywidgets."
    117                 " See https://ipywidgets.readthedocs.io/en/stable"

ImportError: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html

- 에러를 해결하기 위해서는 아래를 설치하면 된다.

# !conda install -c conda-forge ipywidgets -y 
# !conda install -c conda-forge nodejs -y 

- 위를 설치하고 커널을 재시작하면 정상적으로 모형이 만들어진다.

learn=cnn_learner(dls,resnet34,metrics=error_rate)

learn.fine_tune(1)

epoch	train_loss	valid_loss	error_rate	time
0	0.144466	0.020612	0.008119	00:09

epoch	train_loss	valid_loss	error_rate	time
0	0.062860	0.018618	0.005413	00:11

learn.predict(files[0])

('dog', tensor(1), tensor([2.4075e-07, 1.0000e+00]))

learn.show_results()

오답분석

interp = Interpretation.from_learner(learn)

interp.plot_top_losses(16)

진짜 잘되는게 맞는건가?

PILImage.create('2021-09-06-cat1.png')

learn.predict(PILImage.create('2021-09-06-cat1.png'))

('cat', tensor(0), tensor([1.0000e+00, 1.0442e-07]))

- 헷갈리는 고양이 사진인데 잘 구분한다.

PILImage.create('2021-09-06-cat2.jpeg')

learn.predict(PILImage.create('2021-09-06-cat2.jpeg'))

('cat', tensor(0), tensor([1.0000e+00, 3.3645e-06]))

PILImage.create('2021-09-06-hani01.jpeg')

learn.predict(PILImage.create('2021-09-06-hani01.jpeg'))

('dog', tensor(1), tensor([3.1670e-06, 1.0000e+00]))

PILImage.create('2021-09-06-hani02.jpeg')

learn.predict(PILImage.create('2021-09-06-hani02.jpeg'))

('dog', tensor(1), tensor([8.1501e-06, 9.9999e-01]))

PILImage.create('2021-09-06-hani03.jpg')

learn.predict(PILImage.create('2021-09-06-hani03.jpg'))

('dog', tensor(1), tensor([0.0270, 0.9730]))

다음시간

• 이미지 크롤링 --> 데이터셋트 --> A,B 구분하는 모델

숙제

• 위의 사진들 이외에 사진들을 바탕으로 예측을 하는 모형구축.
• 예측결과를 스샷으로 저장하여 제출 (이미지도 함께 스샷할것)

숙제참고자료

import PIL 
urls='https://t1.daumcdn.net/cfile/tistory/9925F03C5AD486B033'
urllib.request.urlretrieve(urls,'temp.png')
learn.predict(PILImage.create('temp.png'))

('dog', tensor(1), tensor([0.0091, 0.9909]))